Frameworks: Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry

利用机器学习（ML）和人工智能（AI）领域的强大新进展是以下方面的关键：1)保持和建立国家在科学和工程领域的竞争力；2)实现健康和医学领域的突破；3)创造未来产业；4)增加经济增长和机会。如今，研究人员利用这些新的ML/AI方法在从材料发现、化学和药物发现到高能物理、天气预报、先进制造和健康等领域的应用中取得了令人兴奋的成果。然而，还有很多工作要做。这些新方法和结果不容易被其他人应用，因为需要专门的专业知识和资源来理解、开发、共享、调整、测试、部署和运行最终的ML/AI模型。为了克服这些阻碍进展的障碍，该项目寻求开发用于构建和创建模型花园的方法和工具，模型花园是与推进特定研究社区工作所需的数据和计算资源相关联的精心策划和测试的ML/AI模型的集合。这样的新方法、软件和工具可以使模型生产者以容易被其他人使用的形式发布模型变得简单，并且模型消费者可以发现已发布的模型并将其集成到学术界或工业界的应用程序中。该项目将材料科学、物理和化学领域的研究人员联系起来，为他们的社区建立模型花园，并授权重点研究中心收集和提供广泛的新方法和模型。此外，该项目促进了有抱负的研究人员与科学问题的联系，吸引了来自不同背景的数百名学生（包括农村社区大学合作伙伴）参与学习，并通过举办研讨会、开放办公时间和开发新的参与平台，为软件开发、模型出版、开发新的人工智能/机器学习应用程序以及培训下一代机器学习/人工智能劳动力做出贡献。该项目通过创建一个新的CSSI框架——花园框架来支持模型花园的建设和运营，从而克服了传播和应用新ML/AI方法的障碍：与推进特定社区工作所需的数据和计算资源相关联的精心策划的模型集合。通过减少与模型发布、发现、访问和部署相关的摩擦；提供数据、模型和代码的有序和结构化的组织和链接；将适当的元数据与模型关联起来，以促进重用和可发现性，并应用质量评估措施（例如，自动化测试、不确定性量化）来支持模型比较；支持围绕特定模型类和研究挑战的社区发展；并且允许在没有（和）下载和安装的情况下轻松访问模型，建立的模型花园减少了使用ML/AI方法的障碍，并促进了围绕特定数据集，方法和模型的社区的形成。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Harnessing powerful new advances in machine learning (ML) and artificial intelligence (AI) is key to 1) maintaining and building national competitiveness in the sciences and engineering, 2) realizing breakthroughs in health and medicine, 3) enabling the creation of industries of the future, and 4) increasing economic growth and opportunity. Today, researchers are achieving exciting results with these new ML/AI methods in applications ranging from materials discovery, chemistry, and drug discovery to high energy physics, weather prediction, advanced manufacturing, and health. Yet, much work remains. These new methods and results are not easily applied by others due to the specialized expertise and resources needed to understand, develop, share, adapt, test, deploy, and run the resulting ML/AI models. To overcome these barriers to progress, this project seeks to develop methods and tools for constructing and creating Model Gardens, collections of curated and tested ML/AI models linked with the data and computing resources required to advance the work of a specific research community. Such new methods, software, and tools can make it simple for model producers to publish models in forms that are easily consumed by others, and for model consumers to discover published models and integrate them into their applications in academia or industry. The project connects researchers in materials science, physics, and chemistry enabling the establishment of Model Gardens for their communities and empowering key research centers to collect and provide broad access to new methods and models resulting from their work. Further, the project facilitates the connection of aspiring researchers with scientific problems, engaging hundreds of students from diverse backgrounds (including rural community college partners) in learning and contributing to software development, model publication, development of new AI/ML applications, and training of a next-generation ML/AI-empowered workforce through hosted workshops, open office hours, and development of a new engagement platform.This project overcomes the barriers to the dissemination and application of new ML/AI methods by creating a new CSSI framework—the Garden Framework to support the construction and operation of Model Gardens: collections of curated models linked with the data and computing resources required to advance the work of specific communities. By reducing the friction associated with model publication, discovery, access, and deployment; providing for the disciplined and structured organization and linking of data, models, and code; associating appropriate metadata with models to promote reuse and discoverability, and applying quality assessment measures (e.g., automated testing, uncertainty quantification) to support model comparison; supporting the development of communities around specific model classes and research challenges; and permitting easy access to models without (and with) download and installation, established Model Gardens reduce barriers to the use of ML/AI methods and promote the nucleation of communities around specific datasets, methods, and models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.